Transfer learning is a well-known solution to the problem of domain shift in which source domain (training set) and target domain (test set) are drawn from different distributions. In the absence of domain shift, discriminative dimensionality reduction approaches could classify target data with acceptable accuracy. However, distribution difference across source and target domains degrades the performance of dimensionalityreduction methods. In this paper, we propose a Discriminative Dimensionality Reduction approach for multi-source Transfer learning, DiReT, in which discrimination is exploited on transferred data. DiReT nds an embedded space, such that the distribution dierenceof the source and target domains is minimized. Moreover, DiReT employs multiple sourcedomains and semi-supervised target domain to transfer knowledge from multiple resources,and it also bridges across source and target domains to nd common knowledge in anembedded space. Empirical evidence of real and articial datasets indicates that DiReTmanages to improve substantially over dimensionality reduction approaches.